Image segmentation techniques are widely used for segmenting medical images and determining contours between anatomical structures within the images. For example, in radiation therapy, automatic segmentation of organs is usually performed to reduce contouring time, and improve contour accuracy and consistency over various hospitals. However, automated segmentation of images remains to be a very difficult task due to noises, limited image contrast and/or low image quality. For example, medical images having lower image quality, such as some computer tomography (CT) or cone-beam computer tomography (CBCT) images that may be used to treat cancer patients, are known to have lower contrast and little texture for most soft tissue structures. Therefore, conventional image segmentation methods based primarily on image contrast often fail to find an accurate contour between the background and anatomical structures (e.g., organs or tumors) of interest, or between different anatomical structures in a medical image.
Two main methods of medical image segmentation include Atlas-based auto-segmentation and statistical learning segmentation. Atlas-based auto-segmentation (ABAS) registers an anatomy labeled image onto an unlabeled image and transfers the labels, wherein the labels identify the anatomic structures in the images (e.g., prostate, bladder, rectum and the like). Statistical learning segmentation classifies image voxels according to the voxel properties of the anatomic structures in the images, wherein the voxel properties include, for example, intensity, texture features and the like.
FIG. 1 illustrates an exemplary three-dimensional (3D) CT image from a typical prostate cancer patient. Illustration (A) shows a pelvic region of the patient in a 3D view, which includes the patient's bladder, prostate, and rectum. Images (B), (C), and (D) are axial, sagittal and coronal views from a 3D CT image of this pelvic region. As shown in images (B), (C), and (D), most part of the patient's prostate boundary is not visible. That is, one cannot readily distinguish the prostate from other anatomical structures or determine a contour for the prostate. In comparison, images (E), (F), and (G) show the expected prostate contour on the same 3D CT image. As illustrated in FIG. 1, conventional image segmentation methods based solely on contrast and textures presented in the image would likely fail when used to segment this exemplary 3D CT image.
Recent developments in machine learning techniques make improved image segmentation on lower quality images possible. For example, supervised learning algorithms can “train” computers to predict the anatomical structure each pixel or voxel of a medical image represents. Such prediction usually uses features of the pixel or voxel as inputs. Therefore, the performance of the segmentation highly depends on the type of features available. To date, most learning-based image segmentation methods are based primarily on local image features such as image intensities and image textures. As a result, these segmentation methods are still suboptimal for lower quality images, such as the 3D CT image shown in FIG. 1.
Accurate segmentation of anatomical structures from CT images remains a challenging problem. Segmented serial images of the same patient have a special utility in adaptive plan review/re-planning and dose accumulation. Serial images necessarily sample different probability distributions than those of populations and therefore ought to provide information that aids the segmentation of new serial images.
Some methods propose combining atlas and statistical methods to gain improved segmentation accuracy. For example, one method bases its on-line learning and patient-specific classification on location-adaptive image contexts. The location-adaptive classifiers are trained on static image appearance features and image context features as well. Such a method has been used on serial prostate images with the stated goal of refining the patient's prostate segmentation as radiotherapy progressed. However, it requires that the data be treated as a three-dimensional data object using the spatial distribution of the voxel patch-pairs as features themselves, and in addition, using a random forest (RF) method.
Alternatively, another method proposes combining bladder volume metric and landmark locations with conventional deformable registration in a study of atlas based segmentation of serial CT images of the cervix-uterus. However, the method can be computationally expensive and error prone since use of landmarks requires human intervention.
Furthermore, another method uses random forests for brain tissue segmentation (in a semi-supervised learning mode) such that the decision forest is trained on anatomy-labeled pre-surgical imagery and unlabeled post-surgical images of the same patient. However, this approach avoids the registration step and thus, fails to take advantage of useful information in Atlas-based registration.
In addition, random forest segmentation has been combined with image registration to perform image segmentation. This allows for inter-patient segmentation, and through the mechanism of an “atlas forest”, multiple patients' images were registered to a common coordinate frame, with one image per forest model. However, the method requires different compositions of training data to provide resulting models.
Further, methods combining deformable registration and random forests are used to segment the bones of the face and teeth from dental Cone Beam CT images. Patient images are registered to an atlas for an initial prediction or estimate of the segmentation. This is followed by a sequence of random forest classifiers that use context features as part of the features to be trained on at each stage of the sequence. However, to the method requires using deformable registration and RF classifiers to form RF models based on prior images of the same patient.
The disclosed methods and systems are designed to solve one or more problems discussed above, by combining Atlas-based segmentation and statistical learning, combining population with intra-patient image and structure information, and enabling convenient updating of a patient segmentation model in order to improve segmentation performance on medical images in radiation therapy or related fields.